TY - THES A1 - Murawski, Aline T1 - Trends in precipitation over Germany and the Rhine basin related to changes in weather patterns T1 - Zeitliche Veränderungen im Niederschlag über Deutschland und dem Rheineinzugsgebiet in Zusammenhang mit Wetterlagen N2 - Niederschlag als eine der wichtigsten meteorologischen Größen für Landwirtschaft, Wasserversorgung und menschliches Wohlbefinden hat schon immer erhöhte Aufmerksamkeit erfahren. Niederschlagsmangel kann verheerende Auswirkungen haben, wie z.B. Missernten und Wasserknappheit. Übermäßige Niederschläge andererseits bergen jedoch ebenfalls Gefahren in Form von Hochwasser oder Sturzfluten und wiederum Missernten. Daher wurde viel Arbeit in die Detektion von Niederschlagsänderungen und deren zugrundeliegende Prozesse gesteckt. Insbesondere angesichts von Klimawandel und unter Berücksichtigung des Zusammenhangs zwischen Temperatur und atmosphärischer Wasserhaltekapazität, ist großer Bedarf an Forschung zum Verständnis der Auswirkungen von Klimawandel auf Niederschlagsänderungen gegeben. Die vorliegende Arbeit hat das Ziel, vergangene Veränderungen in Niederschlag und anderen meteorologischen Variablen zu verstehen. Für verschiedene Zeiträume wurden Tendenzen gefunden und mit entsprechenden Veränderungen in der großskaligen atmosphärischen Zirkulation in Zusammenhang gebracht. Die Ergebnisse dieser Arbeit können als Grundlage für die Attributierung von Hochwasserveränderungen zu Klimawandel genutzt werden. Die Annahmen für die Maßstabsverkleinerung („Downscaling“) der Daten von großskaligen Zirkulationsmodellen auf die lokale Skala wurden hier getestet und verifziert. In einem ersten Schritt wurden Niederschlagsveränderungen in Deutschland analysiert. Dabei lag der Fokus nicht nur auf Niederschlagssummen, sondern auch auf Eigenschaften der statistischen Verteilung, Übergangswahrscheinlichkeiten als Maß für Trocken- und Niederschlagsperioden und Extremniederschlagsereignissen. Den räumlichen Fokus auf das Rheineinzugsgebiet, das größte Flusseinzugsgebiet Deutschlands und einer der Hauptwasserwege Europas, verlagernd, wurden nachgewiesene Veränderungen in Niederschlag und anderen meteorologischen Größen in Bezug zu einer „optimierten“ Wetterlagenklassifikation analysiert. Die Wetterlagenklassifikation wurde unter der Maßgabe entwickelt, die Varianz des lokalen Klimas bestmöglich zu erklären. Die letzte hier behandelte Frage dreht sich darum, ob die beobachteten Veränderungen im lokalen Klima eher Häufigkeitsänderungen der Wetterlagen zuzuordnen sind oder einer Veränderung der Wetterlagen selbst. Eine gebräuchliche Annahme für einen Downscaling-Ansatz mit Hilfe von Wetterlagen und einem stochastischen Wettergenerator ist, dass Klimawandel sich allein durch eine Veränderung der Häufigkeit von Wetterlagen ausdrückt, die Eigenschaften der Wetterlagen dabei jedoch konstant bleiben. Diese Annahme wurde überprüft und die Fähigkeit der neuesten Generation von Zirkulationsmodellen, diese Wetterlagen zu reproduzieren, getestet. Niederschlagsveränderungen in Deutschland im Zeitraum 1951–2006 lassen sich zusammenfassen als negativ im Sommer und positiv in allen anderen Jahreszeiten. Verschiedene Niederschlagscharakteristika bestätigen die Tendenz in den Niederschlagssummen: während mittlere und extreme Niederschlagstageswerte im Winter zugenommen haben, sind auch zusammenhängende Niederschlagsperioden länger geworden (ausgedrückt als eine gestiegene Wahrscheinlichkeit für einen Tag mit Niederschlag gefolgt von einem weiteren nassen Tag). Im Sommer wurde das Gegenteil beobachtet: gesunkene Niederschlagssummen, untermauert von verringerten Mittel- und Extremwerten und längeren Trockenperioden. Abseits dieser allgemeinen Zusammenfassung für das gesamte Gebiet Deutschlands, ist die räumliche Verteilung von Niederschlagsveränderungen deutlich heterogener. Vermehrter Niederschlag im Winter wurde hauptsächlich im Nordwesten und Südosten Deutschlands beobachtet, während im Frühling die stärksten Veränderungen im Westen und im Herbst im Süden aufgetreten sind. Das saisonale Bild wiederum löst sich für die zugehörigen Monate auf, z.B. setzt sich der Anstieg im Herbstniederschlag aus deutlich vermehrtem Niederschlag im Südwesten im Oktober und im Südosten im November zusammen. Diese Ergebnisse betonen die starken räumlichen Zusammenhänge der Niederschlagsänderungen. Der nächste Schritt hinsichtlich einer Zuordnung von Niederschlagsveränderungen zu Änderungen in großskaligen Zirkulationsmustern, war die Ableitung einer Wetterlagenklassifikation, die die betrachteten lokalen Klimavariablen hinreichend stratifizieren kann. Fokussierend auf Temperatur, Globalstrahlung und Luftfeuchte zusätzlich zu Niederschlag, wurde eine Klassifikation basierend auf Luftdruck, Temperatur und spezifischer Luftfeuchtigkeit als am besten geeignet erachtet, die Varianz der lokalen Variablen zu erklären. Eine vergleichsweise hohe Anzahl von 40 Wetterlagen wurde ausgewählt, die es erlaubt, typische Druckmuster durch die zusätzlich verwendete Temperaturinformation einzelnen Jahreszeiten zuzuordnen. Während die Fähigkeit, Varianz im Niederschlag zu erklären, relativ gering ist, ist diese deutlich besser für Globalstrahlung und natürlich Temperatur. Die meisten der aktuellen Zirkulationsmodelle des CMIP5-Ensembles sind in der Lage, die Wetterlagen hinsichtlich Häufigkeit, Saisonalität und Persistenz hinreichend gut zu reproduzieren. Schließlich wurden dieWetterlagen bezüglich Veränderungen in ihrer Häufigkeit, Saisonalität und Persistenz, sowie der Wetterlagen-spezifischen Niederschläge und Temperatur, untersucht. Um Unsicherheiten durch die Wahl eines bestimmten Analysezeitraums auszuschließen, wurden alle möglichen Zeiträume mit mindestens 31 Jahren im Zeitraum 1901–2010 untersucht. Dadurch konnte die Annahme eines konstanten Zusammenhangs zwischen Wetterlagen und lokalem Wetter gründlich überprüft werden. Es wurde herausgefunden, dass diese Annahme nur zum Teil haltbar ist. Während Veränderungen in der Temperatur hauptsächlich auf Veränderungen in der Wetterlagenhäufigkeit zurückzuführen sind, wurde für Niederschlag ein erheblicher Teil von Veränderungen innerhalb einzelner Wetterlagen gefunden. Das Ausmaß und sogar das Vorzeichen der Veränderungen hängt hochgradig vom untersuchten Zeitraum ab. Die Häufigkeit einiger Wetterlagen steht in direkter Beziehung zur langfristigen Variabilität großskaliger Zirkulationsmuster. Niederschlagsveränderungen variieren nicht nur räumlich, sondern auch zeitlich – Aussagen über Tendenzen sind nur in Bezug zum jeweils untersuchten Zeitraum gültig. Während ein Teil der Veränderungen auf Änderungen der großskaligen Zirkulation zurückzuführen ist, gibt es auch deutliche Veränderungen innerhalb einzelner Wetterlagen. Die Ergebnisse betonen die Notwendigkeit für einen sorgfältigen Nachweis von Veränderungen möglichst verschiedene Zeiträume zu untersuchen und mahnen zur Vorsicht bei der Anwendung von Downscaling-Ansätzen mit Hilfe von Wetterlagen, da diese die Auswirkungen von Klimaveränderungen durch das Vernachlässigen von Wetterlagen-internen Veränderungen falsch einschätzen könnten. N2 - Precipitation as the central meteorological feature for agriculture, water security, and human well-being amongst others, has gained special attention ever since. Lack of precipitation may have devastating effects such as crop failure and water scarcity. Abundance of precipitation, on the other hand, may as well result in hazardous events such as flooding and again crop failure. Thus, great effort has been spent on tracking changes in precipitation and relating them to underlying processes. Particularly in the face of global warming and given the link between temperature and atmospheric water holding capacity, research is needed to understand the effect of climate change on precipitation. The present work aims at understanding past changes in precipitation and other meteorological variables. Trends were detected for various time periods and related to associated changes in large-scale atmospheric circulation. The results derived in this thesis may be used as the foundation for attributing changes in floods to climate change. Assumptions needed for the downscaling of large-scale circulation model output to local climate stations are tested and verified here. In a first step, changes in precipitation over Germany were detected, focussing not only on precipitation totals, but also on properties of the statistical distribution, transition probabilities as a measure for wet/dry spells, and extreme precipitation events. Shifting the spatial focus to the Rhine catchment as one of the major water lifelines of Europe and the largest river basin in Germany, detected trends in precipitation and other meteorological variables were analysed in relation to states of an ``optimal'' weather pattern classification. The weather pattern classification was developed seeking the best skill in explaining the variance of local climate variables. The last question addressed whether observed changes in local climate variables are attributable to changes in the frequency of weather patterns or rather to changes within the patterns itself. A common assumption for a downscaling approach using weather patterns and a stochastic weather generator is that climate change is expressed only as a changed occurrence of patterns with the pattern properties remaining constant. This assumption was validated and the ability of the latest generation of general circulation models to reproduce the weather patterns was evaluated. % Paper 1 Precipitation changes in Germany in the period 1951-2006 can be summarised briefly as negative in summer and positive in all other seasons. Different precipitation characteristics confirm the trends in total precipitation: while winter mean and extreme precipitation have increased, wet spells tend to be longer as well (expressed as increased probability for a wet day followed by another wet day). For summer the opposite was observed: reduced total precipitation, supported by decreasing mean and extreme precipitation and reflected in an increasing length of dry spells. Apart from this general summary for the whole of Germany, the spatial distribution within the country is much more differentiated. Increases in winter precipitation are most pronounced in the north-west and south-east of Germany, while precipitation increases are highest in the west for spring and in the south for autumn. Decreasing summer precipitation was observed in most regions of Germany, with particular focus on the south and west. The seasonal picture, however, was again differently represented in the contributing months, e.g.\ increasing autumn precipitation in the south of Germany is formed by strong trends in the south-west in October and in the south-east in November. These results emphasise the high spatial and temporal organisation of precipitation changes. % Paper 2 The next step towards attributing precipitation trends to changes in large-scale atmospheric patterns was the derivation of a weather pattern classification that sufficiently stratifies the local climate variables under investigation. Focussing on temperature, radiation, and humidity in addition to precipitation, a classification based on mean sea level pressure, near-surface temperature, and specific humidity was found to have the best skill in explaining the variance of the local variables. A rather high number of 40 patterns was selected, allowing typical pressure patterns being assigned to specific seasons by the associated temperature patterns. While the skill in explaining precipitation variance is rather low, better skill was achieved for radiation and, of course, temperature. Most of the recent GCMs from the CMIP5 ensemble were found to reproduce these weather patterns sufficiently well in terms of frequency, seasonality, and persistence. % Paper 3 Finally, the weather patterns were analysed for trends in pattern frequency, seasonality, persistence, and trends in pattern-specific precipitation and temperature. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901-2010 with a minimum length of 31 years were considered. Thus, the assumption of a constant link between patterns and local weather was tested rigorously. This assumption was found to hold true only partly. While changes in temperature are mainly attributable to changes in pattern frequency, for precipitation a substantial amount of change was detected within individual patterns. Magnitude and even sign of trends depend highly on the selected time period. The frequency of certain patterns is related to the long-term variability of large-scale circulation modes. Changes in precipitation were found to be heterogeneous not only in space, but also in time - statements on trends are only valid for the specific time period under investigation. While some part of the trends can be attributed to changes in the large-scale circulation, distinct changes were found within single weather patterns as well. The results emphasise the need to analyse multiple periods for thorough trend detection wherever possible and add some note of caution to the application of downscaling approaches based on weather patterns, as they might misinterpret the effect of climate change due to neglecting within-type trends. KW - precipitation KW - weather pattern KW - trend analyses KW - Niederschlag KW - Wetterlagen KW - Trendanalysen Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-412725 ER - TY - GEN A1 - Delgado, José Miguel Martins A1 - Voss, Sebastian A1 - Bürger, Gerd A1 - Vormoor, Klaus Josef A1 - Murawski, Aline A1 - Rodrigues Pereira, José Marcelo A1 - Martins, Eduardo A1 - Vasconcelos Júnior, Francisco A1 - Francke, Till T1 - Seasonal drought prediction for semiarid northeastern Brazil BT - verification of six hydro-meteorological forecast products T2 - Hydrology and Earth System Sciences N2 - A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará’s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 476 KW - Hydrological drought KW - River-Basin KW - Model KW - Patterns KW - Precipitation KW - Variability KW - Nordeste Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-418461 ER - TY - JOUR A1 - Delgado, José Miguel Martins A1 - Voss, Sebastian A1 - Bürger, Gerd A1 - Vormoor, Klaus Josef A1 - Murawski, Aline A1 - Rodrigues Pereira, José Marcelo A1 - Martins, Eduardo A1 - Vasconcelos Júnior, Francisco A1 - Francke, Till T1 - Seasonal drought prediction for semiarid northeastern Brazil BT - verification of six hydro-meteorological forecast products JF - Hydrology and Earth System Sciences N2 - A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará’s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil. KW - Hydrological drought KW - River-Basin KW - Model KW - Patterns KW - Precipitation KW - Variability KW - Nordeste Y1 - 2018 U6 - https://doi.org/10.5194/hess-22-5041-2018 SN - 1027-5606 SN - 1607-7938 VL - 22 IS - 9 SP - 5041 EP - 5056 PB - Copernicus Publ. CY - Göttingen ER - TY - JOUR A1 - Murawski, Aline A1 - Vorogushyn, Sergiy A1 - Bürger, Gerd A1 - Gerlitz, Lars A1 - Merz, Bruno T1 - Do changing weather types explain observed climatic trends in the rhine basin? BT - an analysis of within- and between-type changes JF - Journal of geophysical of geophysical research-atmosheres N2 - For attributing hydrological changes to anthropogenic climate change, catchment models are driven by climate model output. A widespread approach to bridge the spatial gap between global climate and hydrological catchment models is to use a weather generator conditioned on weather patterns (WPs). This approach assumes that changes in local climate are characterized by between-type changes of patterns. In this study we test this assumption by analyzing a previously developed WP classification for the Rhine basin, which is based on dynamic and thermodynamic variables. We quantify changes in pattern characteristics and associated climatic properties. The amount of between- and within-type changes is investigated by comparing observed trends to trends resulting solely from WP occurrence. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901-2010 with a minimum length of 31 years are analyzed. Increasing frequency is found for some patterns associated with high precipitation, although the trend sign highly depends on the considered period. Trends and interannual variations of WP frequencies are related to the long-term variability of large-scale circulation modes. Long-term WP internal warming is evident for summer patterns and enhanced warming for spring/autumn patterns since the 1970s. Observed trends in temperature and partly in precipitation are mainly associated with frequency changes of specific WPs, but some amount of within-type changes remains. The classification can be used for downscaling of past changes considering this limitation, but the inclusion of thermodynamic variables into the classification impedes the downscaling of future climate projections. KW - attribution KW - weather pattern KW - trend analysis KW - downscaling KW - hypothetical trend Y1 - 2018 U6 - https://doi.org/10.1002/2017JD026654 SN - 2169-897X SN - 2169-8996 VL - 123 IS - 3 SP - 1562 EP - 1584 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Murawski, Aline A1 - Bürger, Gerd A1 - Vorogushyn, Sergiy A1 - Merz, Bruno T1 - Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin JF - Hydrology and earth system sciences : HESS N2 - To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis. Y1 - 2016 U6 - https://doi.org/10.5194/hess-20-4283-2016 SN - 1027-5606 SN - 1607-7938 VL - 20 SP - 4283 EP - 4306 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Murawski, Aline A1 - Bürger, Gerd A1 - Vorogushyn, Sergiy A1 - Merz, Bruno T1 - Can local climate variability be explained by weather patterns? BT - a multi-station evaluation for the Rhine basin T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 525 KW - athmospheric circulation patterns KW - stochastic rainfall model KW - within-type variability KW - river Rhine KW - precipitation KW - temperature KW - trends KW - classification KW - Europe KW - scenarios Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-410155 SN - 1866-8372 IS - 525 ER -